Abstract Frequent occurrences of road traffic accidents put forward more and more stringent requirements on the vehicle safety performance. To optimise the vehicle structure crashworthiness, the different optimisation design methods, including deterministic, reliability-based and robust optimisation, are performed simultaneously in this study. The support vector regression (SVR) model is employed to approximate responses between design variables and objectives, and the hybrid kernel function (HKF) is introduced to overcome the drawback of a single kernel function of SVR. Meanwhile, the particle swarm optimisation (PSO) algorithm is adopted to optimise HKF–SVR model parameters and improve the accuracy of the model. By combining the nondominated Sorting Genetic Algorithm II (NSGA-II) and the Monte Carlo Simulation (MCS), the proposed optimisation design approach is proven to be an efficient and systematic tool to guarantee the reliability and robustness of the vehicle structure safety design. These different optimisation design results are discussed and contrasted with initial design. The results show that the proposed approach not only improves the crashworthiness and lightweight of vehicle, but also increases the reliability and robustness of design parameters. Through reliable and robust optimisation, more conservative solutions can be generated.

中文翻译：

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基于优化混合核函数SVR的正面碰撞车辆结构系统优化设计方法

摘要 道路交通事故的频繁发生对车辆的安全性能提出了越来越严格的要求。为了优化车辆结构的耐撞性，本研究同时进行了不同的优化设计方法，包括确定性优化、基于可靠性优化和鲁棒优化。采用支持向量回归（SVR）模型来近似设计变量和目标之间的响应，并引入混合核函数（HKF）来克服支持向量回归（SVR）单核函数的缺点。同时，采用粒子群优化（PSO）算法对HKF-SVR模型参数进行优化，提高模型的精度。通过结合非支配排序遗传算法 II (NSGA-II) 和蒙特卡罗模拟 (MCS)，所提出的优化设计方法被证明是一种有效且系统的工具，可以保证车辆结构安全设计的可靠性和稳健性。这些不同的优化设计结果与初始设计进行了讨论和对比。结果表明，所提出的方法不仅提高了车辆的耐撞性和轻量化，而且增加了设计参数的可靠性和鲁棒性。通过可靠和稳健的优化，可以生成更保守的解决方案。结果表明，所提出的方法不仅提高了车辆的耐撞性和轻量化，而且增加了设计参数的可靠性和鲁棒性。通过可靠和稳健的优化，可以生成更保守的解决方案。结果表明，所提出的方法不仅提高了车辆的耐撞性和轻量化，而且增加了设计参数的可靠性和鲁棒性。通过可靠和稳健的优化，可以生成更保守的解决方案。